AI/ML Technical Lead

We are seeking an experienced Machine Learning Engineer – Technical Lead to join our partner's team. In this role, you will lead the design and delivery of advanced machine learning solutions for industrial IoT applications, while mentoring a team of engineers and data scientists to build scalable, production-ready systems.

Key Responsibilities:

• Design, develop, and deploy machine learning models for:
• predictive maintenance,
• anomaly detection,
• asset optimization,
• and time-series forecasting.
  
• Work with large-scale sensor and telemetry data collected from connected devices.
• Build reliable data pipelines and real-time inference systems integrated across cloud and edge environments.
• Lead the full lifecycle of ML initiatives, from solution design and experimentation to deployment and optimization.
• Provide technical leadership and mentorship to ML and software engineering teams, promoting best practices in model development, testing, and deployment.
• Collaborate closely with product managers, architects, and domain experts to ensure technical solutions align with business objectives.

• Bachelor's degree in Computer Science, Electrical Engineering, Statistics, or a related technical field.
• 5+ years of hands-on experience in machine learning and software engineering.
• Demonstrated experience leading technical teams or complex ML projects in production environments.
• Strong understanding of machine learning and AI concepts, including:
• supervised and unsupervised learning,
• classification,
• regression,
• clustering,
• and deep learning techniques.
  
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
• Strong SQL and cloud platform experience.
• Hands-on experience working with time-series data.
• Excellent communication and cross-functional collaboration skills.

• Master's or PhD in Computer Science, Electrical Engineering, Statistics or a related field.
• Experience working in industrial or manufacturing environments.
• Familiarity with MLOps tools and platforms such as MLflow, Airflow, Docker, and Kubernetes.
• Experience with signal processing, edge computing or physics-informed machine learning models.